Non-parametric recovery of causal diffusion mechanisms from steady-state observations
We consider sparse multivariate stochastic systems that evolve in continuous time according to a causal mechanism and present methodology to recover the system's time-infinitesimal transition mechanism from mere cross-sectional data. This observational paradigm is motivated by applications such as gene expression analysis, where destructive experimental techniques may only allow recording data once over a cell's lifetime. Precisely, we assume the system follows a time-homogeneous diffusion process that has reached an equilibrium distribution at observation time. Further, we assume the causal mechanism is fully described by the diffusion drift, is acyclic, and its causal structure graph is...
| Authors | Richard Schwank, Mathias Drton |
|---|---|
| Primary category | stat.ML |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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